{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "start_time": "2023-07-04T20:25:37.682455Z",
     "end_time": "2023-07-04T20:25:41.983864Z"
    }
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import gym\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "import rl_utils\n",
    "from tqdm import tqdm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "class Qnet(torch.nn.Module):\n",
    "    def __init__(self, state_dim, hidden_dim, action_dim):\n",
    "        super().__init__()\n",
    "        self.fc1 = torch.nn.Linear(state_dim, hidden_dim)\n",
    "        self.fc2 = torch.nn.Linear(hidden_dim, action_dim)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.fc1(x))\n",
    "        return self.fc2(x)\n",
    "class VAnet(torch.nn.Module):\n",
    "    ''' 只有一层隐藏层的A网络和V网络 '''\n",
    "    def __init__(self, state_dim, hidden_dim, action_dim):\n",
    "        super(VAnet, self).__init__()\n",
    "        self.fc1 = torch.nn.Linear(state_dim, hidden_dim)  # 共享网络部分\n",
    "        self.fc_A = torch.nn.Linear(hidden_dim, action_dim)\n",
    "        self.fc_V = torch.nn.Linear(hidden_dim, 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        A = self.fc_A(F.relu(self.fc1(x)))\n",
    "        V = self.fc_V(F.relu(self.fc1(x)))\n",
    "        Q = V + A - A.mean(1).view(-1, 1)  # Q值由V值和A值计算得到\n",
    "        return Q"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-07-04T20:35:16.032037Z",
     "end_time": "2023-07-04T20:35:16.050283Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "class DQN:\n",
    "    \"\"\"\n",
    "    DQN算法，包括Double DQN\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, state_dim, hidden_dim, action_dim, learning_rate, gamma, epsilon, target_update, device,\n",
    "                 dqn_type='VanillaDQN'):\n",
    "        \"\"\"\n",
    "\n",
    "        :param state_dim:\n",
    "        :param hidden_dim:\n",
    "        :param action_dim:\n",
    "        :param learning_rate:\n",
    "        :param gamma:\n",
    "        :param epsilon:\n",
    "        :param target_update:\n",
    "        :param device:\n",
    "        \"\"\"\n",
    "        self.action_dim = action_dim\n",
    "        if dqn_type == 'DuelingDQN':\n",
    "            self.q_net = VAnet(state_dim, hidden_dim, self.action_dim).to(device)\n",
    "            #目标网络\n",
    "            self.target_q_net = VAnet(state_dim, hidden_dim, self.action_dim).to(device)\n",
    "        else:\n",
    "            self.q_net = Qnet(state_dim, hidden_dim, self.action_dim).to(device)\n",
    "            #目标网络\n",
    "            self.target_q_net = Qnet(state_dim, hidden_dim, self.action_dim).to(device)\n",
    "        # 使用Adam优化器\n",
    "        self.optimizer = torch.optim.Adam(self.q_net.parameters(), lr=learning_rate)\n",
    "        self.gamma = gamma  # 折扣因子\n",
    "        self.epsilon = epsilon  # epsilon-贪婪策略\n",
    "        # 目标网络更新频率\n",
    "        self.target_update = target_update\n",
    "        self.count = 0\n",
    "        self.device = device\n",
    "        self.dqn_type = dqn_type\n",
    "\n",
    "    def take_action(self, state):\n",
    "        \"\"\"\n",
    "        epsilon-贪婪策略\n",
    "        :param state:\n",
    "        :return:\n",
    "        \"\"\"\n",
    "        if np.random.random() < self.epsilon:\n",
    "            action = np.random.randint(self.action_dim)\n",
    "        else:\n",
    "            state = torch.tensor([state], dtype=torch.float).to(self.device)\n",
    "            action = self.q_net(state).argmax().item()\n",
    "        return action\n",
    "\n",
    "    def max_q_value(self, state):\n",
    "        state = torch.tensor([state], dtype=torch.float).to(self.device)\n",
    "        return self.q_net(state).max().item()\n",
    "\n",
    "    def update(self, transition_dict):\n",
    "        \"\"\"\n",
    "        核心策略\n",
    "        :param transition_dict:\n",
    "        :return:\n",
    "        \"\"\"\n",
    "        states = torch.tensor(transition_dict['states'], dtype=torch.float).to(self.device)\n",
    "        actions = torch.tensor(transition_dict['actions']).view(-1, 1).to(self.device)\n",
    "        rewards = torch.tensor(transition_dict['rewards'], dtype=torch.float).view(-1, 1).to(self.device)\n",
    "        next_states = torch.tensor(transition_dict['next_states'], dtype=torch.float).to(self.device)\n",
    "        dones = torch.tensor(transition_dict['dones'], dtype=torch.float).view(-1, 1).to(self.device)\n",
    "        q_values = self.q_net(states).gather(1, actions)  # Q值\n",
    "        # 下个状态下的最大Q值\n",
    "        if self.dqn_type == 'DoubleDQN':  # DQN与Double DQN的区别\n",
    "            max_action = self.q_net(next_states).max(dim=1)[1].view(-1, 1)\n",
    "            max_next_q_values = self.target_q_net(next_states).gather(1, max_action)\n",
    "        else:\n",
    "            max_next_q_values = self.target_q_net(next_states).max(dim=1)[0].view(-1, 1)\n",
    "        #TD误差目标|\n",
    "        q_targets = rewards + self.gamma * max_next_q_values * (1 - dones)\n",
    "        # 均方误差损失函数\n",
    "        dqn_loss = torch.mean(F.mse_loss(q_values, q_targets))\n",
    "        # Pytorch中默认梯度会积累，这里需要显式将梯度置于0\n",
    "        self.optimizer.zero_grad()\n",
    "        # 反向传播更新参数\n",
    "        dqn_loss.backward()\n",
    "        self.optimizer.step()\n",
    "\n",
    "        if self.count % self.target_update == 0:\n",
    "            # 更新目标网络\n",
    "            self.target_q_net.load_state_dict(self.q_net.state_dict())\n",
    "        self.count += 1"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-07-04T20:25:42.001815Z",
     "end_time": "2023-07-04T20:25:42.028780Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\PCL\\PyCharm 2023.1\\jbr\\bin\\D\\code\\env\\lib\\site-packages\\gym\\core.py:317: DeprecationWarning: \u001B[33mWARN: Initializing wrapper in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.\u001B[0m\n",
      "  deprecation(\n",
      "E:\\PCL\\PyCharm 2023.1\\jbr\\bin\\D\\code\\env\\lib\\site-packages\\gym\\wrappers\\step_api_compatibility.py:39: DeprecationWarning: \u001B[33mWARN: Initializing environment in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.\u001B[0m\n",
      "  deprecation(\n"
     ]
    }
   ],
   "source": [
    "lr = 2e-3\n",
    "num_episodes = 200\n",
    "hidden_dim = 128\n",
    "gamma = 0.98\n",
    "epsilon = 0.01\n",
    "target_update = 50\n",
    "buffer_size = 5000\n",
    "minimal_size = 1000\n",
    "batch_size = 64\n",
    "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
    "env_name = 'Pendulum-v1'\n",
    "env = gym.make(env_name)\n",
    "state_dim = env.observation_space.shape[0]\n",
    "# 将连续的动作分成11个离散动作\n",
    "action_dim = 11"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-07-04T20:25:42.022760Z",
     "end_time": "2023-07-04T20:25:42.207746Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "def dis_to_con(discrete_action, env, action_dim):\n",
    "    \"\"\"\n",
    "    离散动作转回连续的函数\n",
    "    :param discrete_action:\n",
    "    :param env:\n",
    "    :param action_dim:\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    # 连续动作的最小值\n",
    "    action_lowbound = env.action_space.low[0]\n",
    "    # 连续动作的最大值\n",
    "    action_upbound = env.action_space.high[0]\n",
    "    return action_lowbound + (discrete_action / (action_dim - 1)) * (action_upbound - action_lowbound)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-07-04T20:25:42.204754Z",
     "end_time": "2023-07-04T20:25:42.227694Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "def train_DQN(agent, env, num_episodes, replay_buffer, minimal_size,\n",
    "              batch_size):\n",
    "    return_list = []\n",
    "    max_q_value_list = []\n",
    "    max_q_value = 0\n",
    "    for i in range(10):\n",
    "        with tqdm(total=int(num_episodes / 10),\n",
    "                  desc='Iteration %d' % i) as pbar:\n",
    "            for i_episode in range(int(num_episodes / 10)):\n",
    "                episode_return = 0\n",
    "                state = env.reset()\n",
    "                done = False\n",
    "                while not done:\n",
    "                    action = agent.take_action(state)\n",
    "                    max_q_value = agent.max_q_value(\n",
    "                        state) * 0.005 + max_q_value * 0.995  # 平滑处理\n",
    "                    max_q_value_list.append(max_q_value)  # 保存每个状态的最大Q值\n",
    "                    # Double DQN的区别\n",
    "                    action_continuous = dis_to_con(action, env,\n",
    "                                                   agent.action_dim)\n",
    "                    next_state, reward, done, _ = env.step([action_continuous])\n",
    "                    replay_buffer.add(state, action, reward, next_state, done)\n",
    "                    state = next_state\n",
    "                    episode_return += reward\n",
    "                    if replay_buffer.size() > minimal_size:\n",
    "                        b_s, b_a, b_r, b_ns, b_d = replay_buffer.sample(\n",
    "                            batch_size)\n",
    "                        transition_dict = {\n",
    "                            'states': b_s,\n",
    "                            'actions': b_a,\n",
    "                            'next_states': b_ns,\n",
    "                            'rewards': b_r,\n",
    "                            'dones': b_d\n",
    "                        }\n",
    "                        agent.update(transition_dict)\n",
    "                return_list.append(episode_return)\n",
    "                if (i_episode + 1) % 10 == 0:\n",
    "                    pbar.set_postfix({\n",
    "                        'episode':\n",
    "                        '%d' % (num_episodes / 10 * i + i_episode + 1),\n",
    "                        'return':\n",
    "                        '%.3f' % np.mean(return_list[-10:])\n",
    "                    })\n",
    "                pbar.update(1)\n",
    "    return return_list, max_q_value_list"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-07-04T20:25:42.219714Z",
     "end_time": "2023-07-04T20:25:42.309300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Iteration 0: 100%|██████████| 20/20 [00:11<00:00,  1.74it/s, episode=20, return=-1165.697]\n",
      "Iteration 1: 100%|██████████| 20/20 [00:13<00:00,  1.45it/s, episode=40, return=-1089.202]\n",
      "Iteration 2: 100%|██████████| 20/20 [00:13<00:00,  1.44it/s, episode=60, return=-921.954] \n",
      "Iteration 3: 100%|██████████| 20/20 [00:14<00:00,  1.38it/s, episode=80, return=-1042.062]\n",
      "Iteration 4: 100%|██████████| 20/20 [00:14<00:00,  1.42it/s, episode=100, return=-501.822]\n",
      "Iteration 5: 100%|██████████| 20/20 [00:13<00:00,  1.44it/s, episode=120, return=-200.298]\n",
      "Iteration 6: 100%|██████████| 20/20 [00:13<00:00,  1.44it/s, episode=140, return=-393.549]\n",
      "Iteration 7: 100%|██████████| 20/20 [00:14<00:00,  1.40it/s, episode=160, return=-512.724]\n",
      "Iteration 8: 100%|██████████| 20/20 [00:13<00:00,  1.45it/s, episode=180, return=-383.375]\n",
      "Iteration 9: 100%|██████████| 20/20 [00:14<00:00,  1.39it/s, episode=200, return=-248.497]\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "random.seed(0)\n",
    "np.random.seed(0)\n",
    "env.seed(0)\n",
    "torch.manual_seed(0)\n",
    "replay_buffer = rl_utils.ReplayBuffer(buffer_size)\n",
    "agent = DQN(state_dim, hidden_dim, action_dim, lr, gamma, epsilon,\n",
    "            target_update, device,'DuelingDQN')\n",
    "return_list, max_q_value_list = train_DQN(agent, env, num_episodes,\n",
    "                                          replay_buffer, minimal_size,\n",
    "                                          batch_size)\n",
    "\n",
    "episodes_list = list(range(len(return_list)))\n",
    "mv_return = rl_utils.moving_average(return_list, 5)\n",
    "plt.plot(episodes_list, mv_return)\n",
    "plt.xlabel('Episodes')\n",
    "plt.ylabel('Returns')\n",
    "plt.title('DuelingDQN on {}'.format(env_name))\n",
    "plt.show()\n",
    "\n",
    "frames_list = list(range(len(max_q_value_list)))\n",
    "plt.plot(frames_list, max_q_value_list)\n",
    "plt.axhline(0, c='orange', ls='--')\n",
    "plt.axhline(10, c='red', ls='--')\n",
    "plt.xlabel('Frames')\n",
    "plt.ylabel('Q value')\n",
    "plt.title('DuelingDQN on {}'.format(env_name))\n",
    "plt.show()"
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